Search Results for author:
Found 6 papers, 4 papers with code
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature.
Ranked #2 on
Anomaly Detection
on CUHK Avenue
VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web
The DarkWeb represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services.
Rethinking the Authorship Verification Experimental Setups
One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset.
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.
Ranked #2 on
Anomaly Detection
on UCSD Peds2
Abnormal Event Detection In Video
Anomaly Detection In Surveillance Videos
+4
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
To generate useful data samples for training the student, our framework (i) learns to generate images on a proxy data set (with images and classes different from those used to train the black-box) and (ii) applies an evolutionary strategy to make sure that each generated data sample exhibits a high response for a specific class when given as input to the black box.
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI
In this work, we focus on explainable AI and propose a novel generic and model-agnostic framework for synthesizing input exemplars that maximize a desired response from a machine learning model.
